http://www.lptms.universite-paris-saclay.fr//wiki-cours/api.php?action=feedcontributions&feedformat=atom&user=Mmedenjak 2026-05-17T04:40:52Z User contributions MediaWiki 1.43.6 http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1379 2020-12-02T16:14:02Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (30 points)<br /> <br /> <br /> ''' Here you can find a trial of the MCQ''' [https://drive.google.com/file/d/1ShlhE6PnR34SVGeT3aoJAuyg5n3QCUZl/view?usp=sharing 10 Questions about numerical physics]<br /> <br /> '''you can find a short presentation video of the MCQ on the Dropbox by A. Rosso'''<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> <br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Homework 3''' Due by December 4, 2020<br /> [https://colab.research.google.com/drive/1NkJPKqkut-7Vbr-0jtiyi1tXarvxLUsU?usp=sharing Homework]<br /> [https://drive.google.com/file/d/1Nts8Dc06QjZ7E2uFPj4D6YHVZTTgJeih/view?usp=sharing Data]<br /> <br /> <br /> <br /> '''Friday, November 27, 2020'''<br /> <br /> Tomorrow Florent cannot really give the talk in direct live ... but never fear: <br /> * he can make a short Q/A tomorrow at, say, 15h or 15H30 <br /> <br /> * he registered the whole lecture in video, and put it here:<br /> <br /> [https://www.dropbox.com/s/1yvmqbb5bb67w8n/video_lec4.mov?dl=0 Lecture 11] <br /> <br /> [https://www.dropbox.com/s/dl31z2306y9salr/ML-lec4.pdf?dl=0 The notes] <br /> <br /> <br /> <br /> [https://colab.research.google.com/drive/1OfxV5oL-9CVOxuhKgUF8AsboB89fm2xQ?usp=sharing Tutorial 11] Deep neural networks<br /> [https://colab.research.google.com/drive/1Zblg4v9RE-zcIgIHI3It9Kw8EmzcvwjR?usp=sharing problems]<br /> <br /> <br /> <br /> '''Friday, December 4, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1yxct9k6f2BioBQ6OywN22yfeDtGH_SlJ?usp=sharing Tutorial 12] Convolutional neural networks and auto-encoders<br /> <br /> '''Due: Homework 3''' (send it to me (Marko))<br /> <br /> <br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1373 2020-11-24T17:38:37Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (30 points)<br /> <br /> <br /> ''' Here you can find a trial of the MCQ''' [https://drive.google.com/file/d/1ShlhE6PnR34SVGeT3aoJAuyg5n3QCUZl/view?usp=sharing 10 Questions about numerical physics]<br /> <br /> '''you can find a short presentation video of the MCQ on the Dropbox by A. Rosso'''<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> <br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Homework 3''' Due by December 4, 2020<br /> [https://colab.research.google.com/drive/1NkJPKqkut-7Vbr-0jtiyi1tXarvxLUsU?usp=sharing Homework]<br /> [https://drive.google.com/file/d/1Nts8Dc06QjZ7E2uFPj4D6YHVZTTgJeih/view?usp=sharing Data]<br /> <br /> <br /> <br /> '''Friday, November 27, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1OfxV5oL-9CVOxuhKgUF8AsboB89fm2xQ?usp=sharing Tutorial 11] Deep neural networks<br /> [https://colab.research.google.com/drive/1Zblg4v9RE-zcIgIHI3It9Kw8EmzcvwjR?usp=sharing problems]<br /> <br /> <br /> <br /> '''Friday, December 4, 2020'''<br /> <br /> Modern neural networks<br /> <br /> '''Due: Homework 3''' (send it to me (Marko))<br /> <br /> <br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1370 2020-11-17T14:41:53Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (30 points)<br /> <br /> <br /> ''' Here you can find a trial of the MCQ''' [https://drive.google.com/file/d/1ShlhE6PnR34SVGeT3aoJAuyg5n3QCUZl/view?usp=sharing 10 Questions about numerical physics]<br /> <br /> '''you can find a short presentation video of the MCQ on the Dropbox by A. Rosso'''<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Homework 3''' Due by December 4, 2020<br /> [https://colab.research.google.com/drive/1NkJPKqkut-7Vbr-0jtiyi1tXarvxLUsU?usp=sharing Homework]<br /> [https://drive.google.com/file/d/1Nts8Dc06QjZ7E2uFPj4D6YHVZTTgJeih/view?usp=sharing Data]<br /> <br /> '''Friday, November 27, 2020'''<br /> <br /> Kernel methods and neural networks<br /> <br /> '''Friday, December 4, 2020'''<br /> <br /> Modern neural networks<br /> <br /> '''Due: Homework 3''' (send it to me (Marko))<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1369 2020-11-17T14:39:26Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (30 points)<br /> <br /> <br /> ''' Here you can find a trial of the MCQ''' [https://drive.google.com/file/d/1ShlhE6PnR34SVGeT3aoJAuyg5n3QCUZl/view?usp=sharing 10 Questions about numerical physics]<br /> <br /> '''you can find a short presentation video of the MCQ on the Dropbox by A. Rosso'''<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Homework 3''' Due by December 4, 2020<br /> [https://colab.research.google.com/drive/1NkJPKqkut-7Vbr-0jtiyi1tXarvxLUsU?usp=sharing Homework]<br /> [https://drive.google.com/file/d/1Nts8Dc06QjZ7E2uFPj4D6YHVZTTgJeih/view?usp=sharing Data]<br /> <br /> '''Friday, November 27, 2020'''<br /> <br /> Kernel methods and neural networks<br /> <br /> '''Friday, December 4, 2020'''<br /> <br /> Modern neural networks<br /> <br /> Due: Homework 3 (send it to me (Marko))<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1364 2020-11-13T13:15:27Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1363 2020-11-13T13:14:31Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> '''Friday, November 13, 2020'''<br /> <br /> Lecture 10: <br /> <br /> [https://colab.research.google.com/drive/1gpDaNKhg3vZqbsYPJqPSZCgboHfgki3F?usp=sharing Tutorial 10]: k-NN and regression [https://colab.research.google.com/drive/1d66oT7a5JuIHgHJjAbEsUQ9IccCrASsk?usp=sharing problems]<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1357 2020-11-03T17:48:52Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation: follow the link [https://epfl.zoom.us/my/krzakalaflorent]<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> [https://colab.research.google.com/drive/1pUeAtNXo-eLyTs9Kl6_fchsqsBxeEiKg?usp=sharing Tutorial 9]: Restricted Boltzmann machines [https://colab.research.google.com/drive/1rctxia3v3y_AMOXYIHh3hL6NuxmbL2CB?usp=sharing problems]<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1353 2020-10-19T18:54:07Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting/lecture room.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelyhood estimation<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelyhood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1352 2020-10-19T09:49:17Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on https://epfl.zoom.us/my/krzakalaflorent Tutorials will take place as usual on the gotomeeting/lecture room.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelihood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1351 2020-10-19T09:48:38Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> '''Lectures on machine learning will be done remotely. You will be able to access them on<br /> https://epfl.zoom.us/my/krzakalaflorent<br /> Tutorials will take place as usual on the gotomeeting/lecture room.'''<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelihood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1350 2020-10-19T09:44:27Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> [https://colab.research.google.com/drive/1m2YBrQLfhVSMBpTEsfvOf4hXVZplp1D7?usp=sharing#scrollTo=-cLvGNzqh2Ah Tutorial 8]: Maximum Likelihood estimation [https://colab.research.google.com/drive/1p2ZcgOUPcUS3LGP3k7-N8ZBbA2FM4qrs?usp=sharing problems]<br /> <br /> ''' Due: Homework 2 (send it to Marko)'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1347 2020-10-15T19:26:52Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> Tutorial 8: Maximum Likelihood estimation<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1346 2020-10-15T19:26:13Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing [https://colab.research.google.com/drive/1txJGpzreHurWux7ev6sDsX8TjzpeQhZZ?usp=sharing problems]<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> Tutorial 8: Maximum Likelihood estimation<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1345 2020-10-15T17:07:40Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> Tutorial 8: Maximum Likelihood estimation<br /> <br /> ''' Due: Homework 2'''<br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1344 2020-10-15T17:07:01Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> ''' Project in Markox Chain Monte Carlo''' [https://drive.google.com/file/d/1YHERixzqAwGHeEHZYIZwHXl11Ezt_m3h/view?usp=sharing Manon Michel's Project]<br /> <br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> [https://drive.google.com/file/d/1eY5cuNcJqYw7df0PBkxq03tEnqzTOC-5/view?usp=sharing Lecture 6]: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> <br /> [https://drive.google.com/file/d/1qAG8ARVuuXjMzQkU8I92KUEEPZxK5ynr/view?usp=sharing Lecture 7]: Optimization &amp; Dijkstra algorithm<br /> <br /> [https://colab.research.google.com/drive/1p0ooWAXF9KNh-FztMHmVoA9yBzYOM8PC?usp=sharing Tutorial 7]: Simulated annealing<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> Lecture 8: Maximum Likelihood estimation<br /> <br /> Tutorial 8: Maximum Likelihood estimation<br /> <br /> ''' Due: Homework 2'''<br /> <br /> '''Friday, November 06, 2020'''<br /> <br /> Lecture 9: Restricted Boltzmann machines<br /> <br /> TUtorial 9: Restricted Boltzmann machines<br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1337 2020-10-08T14:07:19Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> Do you want to discuss with us during the homeworks? <br /> <br /> '''Slack Forum's link''' [https://join.slack.com/t/mastericfp-ens-paris/shared_invite/zt-h99lnbzo-aXKoDXd9Q5R8kKmk9UlikA]. '''Last Call'''<br /> <br /> ''' Due: Homework 1'''<br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 6: Importance sampling<br /> <br /> [https://colab.research.google.com/drive/1KyT442vB4EuGKBy_Xj3j8cLtMMl4gbac?usp=sharing Tutorial 6]: Faster than the clock algorithms [https://colab.research.google.com/drive/1C-ieAwaAKwkX5MFm8aX2kNO2GNeO9CdY?usp=sharing problems]<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 7: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 7: Simulated annealing<br /> <br /> <br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> MACHINE LEARNING CLASS<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> '''Friday, December 11, 2020'''<br /> <br /> '''Multiple Choice Questions: the final test'''<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1329 2020-10-01T18:11:16Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> Do you want to discuss with us during the homeworks? <br /> <br /> '''Slack Forum's link''' [https://join.slack.com/t/mastericfp-ens-paris/shared_invite/zt-h99lnbzo-aXKoDXd9Q5R8kKmk9UlikA]. '''Last Call'''<br /> <br /> ''' Due: Homework 1'''<br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2: [https://drive.google.com/file/d/1lTgHlAvWAKhkZK13_yihmPxGzD3qo7ko/view?usp=sharing Download]<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 6: Importance sampling<br /> <br /> Tutorial 6: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 7: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 7: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> ?????????????????????????<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1328 2020-10-01T15:13:55Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions <br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] <br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> '''GoToMeeting link''' [https://global.gotomeeting.com/join/854835733] (Room 1 M2 ICFP)<br /> <br /> Do you want to discuss with us during the homeworks? <br /> <br /> '''Slack Forum's link''' [https://join.slack.com/t/mastericfp-ens-paris/shared_invite/zt-h99lnbzo-aXKoDXd9Q5R8kKmk9UlikA]. '''Last Call'''<br /> <br /> ''' Due: Homework 1'''<br /> <br /> [https://drive.google.com/file/d/1JY7PlB00hGpw1814lUyVor37E-um3xQj/view?usp=sharing Lecture 5]: Quantum particle<br /> <br /> [https://colab.research.google.com/drive/1mYLzPTV79kpesX2ZR8LlQfdyGva45o9p?usp=sharing Tutorial 5]: Time evolution (quantum) [https://colab.research.google.com/drive/142acFqwTI91RZT_9gSb1-JHs9rd2PO_E?usp=sharing problems]<br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 6: Importance sampling<br /> <br /> Tutorial 6: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 7: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 7: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> ?????????????????????????<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1323 2020-09-25T12:42:17Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Today we try with '''GoToMeeting''' (GoToMeeting link [https://global.gotomeeting.com/join/854835733] Room 1 M2 ICFP). Zoom's links are just in case...<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions [https://colab.research.google.com/drive/1AEUX3U7hxDR_YYy8ld3knTEVR1KYa9SP?usp=sharing problems] (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1319 2020-09-25T00:27:54Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems]<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1nU4E_pFWjSFPNigzy8-LcJpef26IKY6x?usp=sharing Tutorial 4]: Ising model and phase transitions (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1315 2020-09-21T12:36:31Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> = IMPORTANT=<br /> <br /> <br /> ''' Starting from Friday (25-09-2020) we are changing lecture&amp;tutorial's room. Our new room is L367 (third floor)'''<br /> <br /> The advantage of the new location is that L367 is better equipped (the camera, the video-projector...). The disadvantage is that L367 can only accommodate 24 students and we are slightly overbooking your presence, but from the experience of these weeks we think it is OK. <br /> <br /> '''If you want to come on Friday, we ask you to write your name in the list below:'''<br /> <br /> [https://docs.google.com/spreadsheets/d/1V7LLpfvIm1becvJyoz0BLVEdkWrSHk2YdhrzNcpOfaE/edit?usp=sharing List of participants] <br /> <br /> If you do not find a place, please let us know. Thank you very much for your cooperation!<br /> <br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room L367 (third floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems] (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1DijhG_856OAuj42WqY1UM47APmMy3A8B/view?usp=sharing Lecture 4]: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1310 2020-09-18T13:14:33Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room Conf IV (2nd floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule [https://colab.research.google.com/drive/1fdKJlp0lD4k530oRuu50_Hm_T8t3jixa?usp=sharing problems] (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Lecture 3: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1309 2020-09-18T12:24:45Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room Conf IV (2nd floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix [https://colab.research.google.com/drive/1Q5ajzxRGXBBorA9cQ9o8V5VUVJhUXpPS?usp=sharing with problems]<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> Homework: [https://drive.google.com/file/d/1g7HXFUBQUBF0fhy5h2jYbfIAVBTqvb7-/view?usp=sharing Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Lecture 3: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1307 2020-09-17T16:00:48Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room Conf IV (2nd floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> <br /> * [https://drive.google.com/file/d/15wrgivn6FSnuBUnMwjjhadmD-g1fkS7T/view?usp=sharing Lecture 2] Basic Sampling<br /> <br /> * [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2] Markov matrix<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> * [https://drive.google.com/file/d/1ALR_QKLXdHby54xNux39NUOk4L-Ne1EW/view?usp=sharing Lecture 3]: Errors and Precision (Zoom link [https://zoom.us/j/97382020537?pwd=MXNTdWoxLy9Od0lONDhnU2tXVlJXdz09])<br /> <br /> * [https://colab.research.google.com/drive/1k60-ChM3aUWjGsiRsf2Idx9jxHHtwyAb?usp=sharing Tutorial 3] Thumb rule (Zoom link [https://zoom.us/j/97033444403?pwd=NzJPQ3Yxa2JrQW56aVlGOGV0Mk40dz09])<br /> <br /> Homework: [ Download]<br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Lecture 3: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1293 2020-09-10T15:28:24Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room Conf IV (2nd floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1] Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> <br /> Zoom link [https://zoom.us/j/91787445341?pwd=YzJiazFhdGpQeCtqeHgwQllqQTlSQT09]<br /> <br /> Lecture 2: Basic Sampling<br /> <br /> [https://colab.research.google.com/drive/1aMo4Ur-0_b4dGhdIfxAmCC2ci1rOda_v?usp=sharing Tutorial 2:] Markov matrix<br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> Lecture 2: Error evaluation<br /> <br /> Tutorial 2: Thumb rule<br /> <br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Lecture 3: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak http://www.lptms.universite-paris-saclay.fr//wiki-cours/index.php?title=NUMPHYsandML&diff=1291 2020-09-09T15:22:48Z

<p>Mmedenjak: </p> <hr /> <div>= Numerical Physics and Machine Learning =<br /> <br /> == Course description == <br /> <br /> We will cover many algothims used in many-body problems and complex systems: Monte Carlo methods, molecular dynamics and optmization in complex landscapes. We shall also discuss the use of some machine learning algorithms (Boltzmann machines, Auto-encoder, Deep Learning) for physics problems.<br /> We focus on algorithms and physics, not on programming and heavy numerics. The theoretical lecture is followed by a tutorial introducing concrete numerical exercises. You will have to hand in 3 homeworks.<br /> <br /> <br /> == The Team ==<br /> <br /> * [http://lptms.u-psud.fr/alberto_rosso/ Alberto Rosso] (Numerical Physics)<br /> * [https://florentkrzakala.com/ Florent Krzakala] (Machine Learning)<br /> * Marko Medenjak (Tutorials)<br /> <br /> == Where and When ==<br /> <br /> * Lectures on Fridays: 14:00-16:00<br /> * Tutorials on Fridays: 16:00-18:00<br /> * ENS, 24 rue Lhomond, room Conf IV (2nd floor)<br /> <br /> <br /> == Computer Requirements ==<br /> <br /> '''No previous experience in programming is required.''' &lt;br&gt;<br /> <br /> '''Programming Language: Python'''<br /> <br /> For practical installation, we recommand either to use Anaconda (See [[Memento Python]]) or use google colab. &lt;br&gt;<br /> The Colaboratory platform from Google is quite good way to use powerful computer without buying one: It requires no specific hardware or software, and even allows you to use GPU computing for free, all by writting a jupyter notebook that you can then share.<br /> <br /> == Grading ==<br /> <br /> Homeworks (10 points each) + 1 MCQ (20 points)<br /> <br /> == Schedule ==<br /> <br /> <br /> '''Friday, September 4, 2020 '''<br /> <br /> Zoom link [https://zoom.us/j/4583355667 https://zoom.us/j/4583355667]<br /> <br /> * [https://drive.google.com/file/d/1X9h3lKD0OZLTKtxb7DWPfynY42rRuE7j/view?usp=sharing Lecture 1] Introduction to Monte Carlo<br /> <br /> * [https://drive.google.com/file/d/1sIv3qOdyE-XYkjYC-dkNsR72f4BgaQ_y/view?usp=sharing Tutorial 1]: Markov Matrix<br /> <br /> '''Friday, September 11, 2020'''<br /> <br /> Tutorial 2: Markov matrix &amp; thumb rule<br /> <br /> Homework 1: Page Rank &amp; Errors<br /> <br /> <br /> <br /> '''Friday, September 18, 2020'''<br /> <br /> Lecture 2: Error evaluation<br /> <br /> Tutorial 2: Sampling non uniform distribution<br /> <br /> <br /> <br /> '''Friday, September 25, 2020'''<br /> <br /> Lecture 3: Ising model and phase transitions<br /> <br /> Tutorial 3: Ising model and phase transitions<br /> <br /> ''' Due: Homework 1'''<br /> <br /> <br /> '''Friday, October 2, 2020'''<br /> <br /> Lecture 3: Quantum particle<br /> <br /> Tutorial 3: Time evolution (quantum)<br /> <br /> Homework 2:<br /> <br /> <br /> <br /> '''Friday, October 9, 2020'''<br /> <br /> Lecture 4: Importance sampling<br /> <br /> Tutorial 4: Faster than the clock algorithms<br /> <br /> <br /> <br /> ''' Friday, October 16, 2020'''<br /> <br /> Lecture 5: Optimization &amp; Dijkstra algorithm<br /> <br /> Tutorial 5: Simulated annealing<br /> <br /> ''' Due: Homework 2'''<br /> <br /> <br /> <br /> '''Friday, October 23, 2020'''<br /> <br /> '''QCM: 2 hours, 20 questions for 20 points'''<br /> <br /> Lecture 5: more on ptimization<br /> <br /> == References ==<br /> * [http://www.lps.ens.fr/~krauth/index.php/SMAC SMAC W. Krauth Statistical Mechanics: Algorithms and Computations (Oxford: Oxford University Press) (2006)]<br /> * Other references are specified in each lectures</div>

Mmedenjak